{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"color:blue\" align=\"center\">Pandas Time Series Tutorial: DateTimeIndex</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>142.90</td>\n",
       "      <td>144.75</td>\n",
       "      <td>142.90</td>\n",
       "      <td>144.18</td>\n",
       "      <td>19201712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>143.02</td>\n",
       "      <td>143.50</td>\n",
       "      <td>142.41</td>\n",
       "      <td>142.73</td>\n",
       "      <td>24128782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-07-07  142.90  144.75  142.90  144.18  19201712\n",
       "2017-07-06  143.02  143.50  142.41  142.73  24128782"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"aapl.csv\",parse_dates=[\"Date\"], index_col=\"Date\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-07-07', '2017-07-06', '2017-07-05', '2017-07-03',\n",
       "               '2017-06-30', '2017-06-29', '2017-06-28', '2017-06-27',\n",
       "               '2017-06-26', '2017-06-23',\n",
       "               ...\n",
       "               '2016-07-22', '2016-07-21', '2016-07-20', '2016-07-19',\n",
       "               '2016-07-18', '2016-07-15', '2016-07-14', '2016-07-13',\n",
       "               '2016-07-12', '2016-07-11'],\n",
       "              dtype='datetime64[ns]', name='Date', length=251, freq=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:purple\">What is DatetimeIndex? Benefits of it</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> (1) Partial Date Index: Select Specific Months Data</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Volume</th>\n",
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       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>144.45</td>\n",
       "      <td>144.96</td>\n",
       "      <td>143.78</td>\n",
       "      <td>144.02</td>\n",
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       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-06-30  144.45  144.96  143.78  144.02  23024107"
      ]
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     "metadata": {},
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   ],
   "source": [
    "df['2017-06-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>121.15</td>\n",
       "      <td>121.39</td>\n",
       "      <td>120.62</td>\n",
       "      <td>121.35</td>\n",
       "      <td>49200993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>120.93</td>\n",
       "      <td>121.63</td>\n",
       "      <td>120.66</td>\n",
       "      <td>121.63</td>\n",
       "      <td>30377503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>122.14</td>\n",
       "      <td>122.35</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.95</td>\n",
       "      <td>20562944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>121.67</td>\n",
       "      <td>122.44</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.94</td>\n",
       "      <td>26337576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>120.42</td>\n",
       "      <td>122.10</td>\n",
       "      <td>120.28</td>\n",
       "      <td>121.88</td>\n",
       "      <td>32586673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>119.55</td>\n",
       "      <td>120.10</td>\n",
       "      <td>119.50</td>\n",
       "      <td>119.97</td>\n",
       "      <td>23211038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.81</td>\n",
       "      <td>119.77</td>\n",
       "      <td>120.08</td>\n",
       "      <td>22050218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>120.45</td>\n",
       "      <td>120.45</td>\n",
       "      <td>119.73</td>\n",
       "      <td>120.00</td>\n",
       "      <td>32597892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>119.40</td>\n",
       "      <td>120.09</td>\n",
       "      <td>119.37</td>\n",
       "      <td>119.78</td>\n",
       "      <td>25597291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.50</td>\n",
       "      <td>119.71</td>\n",
       "      <td>119.99</td>\n",
       "      <td>23712961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>118.34</td>\n",
       "      <td>120.24</td>\n",
       "      <td>118.22</td>\n",
       "      <td>120.00</td>\n",
       "      <td>34439843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>119.11</td>\n",
       "      <td>119.62</td>\n",
       "      <td>118.81</td>\n",
       "      <td>119.04</td>\n",
       "      <td>26111948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>118.90</td>\n",
       "      <td>119.30</td>\n",
       "      <td>118.21</td>\n",
       "      <td>119.25</td>\n",
       "      <td>27086220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>118.74</td>\n",
       "      <td>119.93</td>\n",
       "      <td>118.60</td>\n",
       "      <td>119.75</td>\n",
       "      <td>27588593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>118.77</td>\n",
       "      <td>119.38</td>\n",
       "      <td>118.30</td>\n",
       "      <td>119.11</td>\n",
       "      <td>24462051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>117.95</td>\n",
       "      <td>119.43</td>\n",
       "      <td>117.94</td>\n",
       "      <td>118.99</td>\n",
       "      <td>33561948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>116.78</td>\n",
       "      <td>118.16</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>115.92</td>\n",
       "      <td>116.86</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
       "      <td>22193587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>115.85</td>\n",
       "      <td>116.51</td>\n",
       "      <td>115.75</td>\n",
       "      <td>116.02</td>\n",
       "      <td>21118116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>115.80</td>\n",
       "      <td>116.33</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
       "      <td>28781865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-01-31  121.15  121.39  120.62  121.35  49200993\n",
       "2017-01-30  120.93  121.63  120.66  121.63  30377503\n",
       "2017-01-27  122.14  122.35  121.60  121.95  20562944\n",
       "2017-01-26  121.67  122.44  121.60  121.94  26337576\n",
       "2017-01-25  120.42  122.10  120.28  121.88  32586673\n",
       "2017-01-24  119.55  120.10  119.50  119.97  23211038\n",
       "2017-01-23  120.00  120.81  119.77  120.08  22050218\n",
       "2017-01-20  120.45  120.45  119.73  120.00  32597892\n",
       "2017-01-19  119.40  120.09  119.37  119.78  25597291\n",
       "2017-01-18  120.00  120.50  119.71  119.99  23712961\n",
       "2017-01-17  118.34  120.24  118.22  120.00  34439843\n",
       "2017-01-13  119.11  119.62  118.81  119.04  26111948\n",
       "2017-01-12  118.90  119.30  118.21  119.25  27086220\n",
       "2017-01-11  118.74  119.93  118.60  119.75  27588593\n",
       "2017-01-10  118.77  119.38  118.30  119.11  24462051\n",
       "2017-01-09  117.95  119.43  117.94  118.99  33561948\n",
       "2017-01-06  116.78  118.16  116.47  117.91  31751900\n",
       "2017-01-05  115.92  116.86  115.81  116.61  22193587\n",
       "2017-01-04  115.85  116.51  115.75  116.02  21118116\n",
       "2017-01-03  115.80  116.33  114.76  116.15  28781865"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2017-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>144.45</td>\n",
       "      <td>144.96</td>\n",
       "      <td>143.78</td>\n",
       "      <td>144.02</td>\n",
       "      <td>23024107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-29</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-28</th>\n",
       "      <td>144.49</td>\n",
       "      <td>146.11</td>\n",
       "      <td>143.16</td>\n",
       "      <td>145.83</td>\n",
       "      <td>22082432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-27</th>\n",
       "      <td>145.01</td>\n",
       "      <td>146.16</td>\n",
       "      <td>143.62</td>\n",
       "      <td>143.73</td>\n",
       "      <td>24761891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-06-30  144.45  144.96  143.78  144.02  23024107\n",
       "2017-06-29  144.71  145.13  142.28  143.68  31499368\n",
       "2017-06-28  144.49  146.11  143.16  145.83  22082432\n",
       "2017-06-27  145.01  146.16  143.62  143.73  24761891\n",
       "2017-06-26  147.17  148.28  145.38  145.82  25692361"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-06'].head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Average price of aapl's stock in June, 2017</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147.8313636363636"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-06'].Close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>2017-07-07</th>\n",
       "      <td>142.90</td>\n",
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      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-07-07  142.90  144.75  142.90  144.18  19201712\n",
       "2017-07-06  143.02  143.50  142.41  142.73  24128782"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017'].head(2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>(2) Select Date Range</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>116.78</td>\n",
       "      <td>118.16</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>115.92</td>\n",
       "      <td>116.86</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
       "      <td>22193587</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>115.85</td>\n",
       "      <td>116.51</td>\n",
       "      <td>115.75</td>\n",
       "      <td>116.02</td>\n",
       "      <td>21118116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>115.80</td>\n",
       "      <td>116.33</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
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       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-01-06  116.78  118.16  116.47  117.91  31751900\n",
       "2017-01-05  115.92  116.86  115.81  116.61  22193587\n",
       "2017-01-04  115.85  116.51  115.75  116.02  21118116\n",
       "2017-01-03  115.80  116.33  114.76  116.15  28781865"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01-08':'2017-01-03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>121.15</td>\n",
       "      <td>121.39</td>\n",
       "      <td>120.62</td>\n",
       "      <td>121.35</td>\n",
       "      <td>49200993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>120.93</td>\n",
       "      <td>121.63</td>\n",
       "      <td>120.66</td>\n",
       "      <td>121.63</td>\n",
       "      <td>30377503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>122.14</td>\n",
       "      <td>122.35</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.95</td>\n",
       "      <td>20562944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>121.67</td>\n",
       "      <td>122.44</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.94</td>\n",
       "      <td>26337576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>120.42</td>\n",
       "      <td>122.10</td>\n",
       "      <td>120.28</td>\n",
       "      <td>121.88</td>\n",
       "      <td>32586673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>119.55</td>\n",
       "      <td>120.10</td>\n",
       "      <td>119.50</td>\n",
       "      <td>119.97</td>\n",
       "      <td>23211038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.81</td>\n",
       "      <td>119.77</td>\n",
       "      <td>120.08</td>\n",
       "      <td>22050218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>120.45</td>\n",
       "      <td>120.45</td>\n",
       "      <td>119.73</td>\n",
       "      <td>120.00</td>\n",
       "      <td>32597892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>119.40</td>\n",
       "      <td>120.09</td>\n",
       "      <td>119.37</td>\n",
       "      <td>119.78</td>\n",
       "      <td>25597291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.50</td>\n",
       "      <td>119.71</td>\n",
       "      <td>119.99</td>\n",
       "      <td>23712961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>118.34</td>\n",
       "      <td>120.24</td>\n",
       "      <td>118.22</td>\n",
       "      <td>120.00</td>\n",
       "      <td>34439843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>119.11</td>\n",
       "      <td>119.62</td>\n",
       "      <td>118.81</td>\n",
       "      <td>119.04</td>\n",
       "      <td>26111948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>118.90</td>\n",
       "      <td>119.30</td>\n",
       "      <td>118.21</td>\n",
       "      <td>119.25</td>\n",
       "      <td>27086220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>118.74</td>\n",
       "      <td>119.93</td>\n",
       "      <td>118.60</td>\n",
       "      <td>119.75</td>\n",
       "      <td>27588593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>118.77</td>\n",
       "      <td>119.38</td>\n",
       "      <td>118.30</td>\n",
       "      <td>119.11</td>\n",
       "      <td>24462051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>117.95</td>\n",
       "      <td>119.43</td>\n",
       "      <td>117.94</td>\n",
       "      <td>118.99</td>\n",
       "      <td>33561948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>116.78</td>\n",
       "      <td>118.16</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>115.92</td>\n",
       "      <td>116.86</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
       "      <td>22193587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>115.85</td>\n",
       "      <td>116.51</td>\n",
       "      <td>115.75</td>\n",
       "      <td>116.02</td>\n",
       "      <td>21118116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>115.80</td>\n",
       "      <td>116.33</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
       "      <td>28781865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-01-31  121.15  121.39  120.62  121.35  49200993\n",
       "2017-01-30  120.93  121.63  120.66  121.63  30377503\n",
       "2017-01-27  122.14  122.35  121.60  121.95  20562944\n",
       "2017-01-26  121.67  122.44  121.60  121.94  26337576\n",
       "2017-01-25  120.42  122.10  120.28  121.88  32586673\n",
       "2017-01-24  119.55  120.10  119.50  119.97  23211038\n",
       "2017-01-23  120.00  120.81  119.77  120.08  22050218\n",
       "2017-01-20  120.45  120.45  119.73  120.00  32597892\n",
       "2017-01-19  119.40  120.09  119.37  119.78  25597291\n",
       "2017-01-18  120.00  120.50  119.71  119.99  23712961\n",
       "2017-01-17  118.34  120.24  118.22  120.00  34439843\n",
       "2017-01-13  119.11  119.62  118.81  119.04  26111948\n",
       "2017-01-12  118.90  119.30  118.21  119.25  27086220\n",
       "2017-01-11  118.74  119.93  118.60  119.75  27588593\n",
       "2017-01-10  118.77  119.38  118.30  119.11  24462051\n",
       "2017-01-09  117.95  119.43  117.94  118.99  33561948\n",
       "2017-01-06  116.78  118.16  116.47  117.91  31751900\n",
       "2017-01-05  115.92  116.86  115.81  116.61  22193587\n",
       "2017-01-04  115.85  116.51  115.75  116.02  21118116\n",
       "2017-01-03  115.80  116.33  114.76  116.15  28781865"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:purple\">Resampling</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-31     99.473333\n",
       "2016-08-31    107.665217\n",
       "2016-09-30    110.857143\n",
       "2016-10-31    115.707143\n",
       "2016-11-30    110.154286\n",
       "Freq: M, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Close'].resample('M').mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-29</th>\n",
       "      <td>104.19</td>\n",
       "      <td>104.55</td>\n",
       "      <td>103.68</td>\n",
       "      <td>104.21</td>\n",
       "      <td>27733688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28</th>\n",
       "      <td>102.83</td>\n",
       "      <td>104.45</td>\n",
       "      <td>102.82</td>\n",
       "      <td>104.34</td>\n",
       "      <td>39869839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-27</th>\n",
       "      <td>104.26</td>\n",
       "      <td>104.35</td>\n",
       "      <td>102.75</td>\n",
       "      <td>102.95</td>\n",
       "      <td>92344820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-26</th>\n",
       "      <td>96.82</td>\n",
       "      <td>97.97</td>\n",
       "      <td>96.42</td>\n",
       "      <td>96.67</td>\n",
       "      <td>56239822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>98.25</td>\n",
       "      <td>98.84</td>\n",
       "      <td>96.92</td>\n",
       "      <td>97.34</td>\n",
       "      <td>40382921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-22</th>\n",
       "      <td>99.26</td>\n",
       "      <td>99.30</td>\n",
       "      <td>98.31</td>\n",
       "      <td>98.66</td>\n",
       "      <td>28313669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-21</th>\n",
       "      <td>99.83</td>\n",
       "      <td>101.00</td>\n",
       "      <td>99.13</td>\n",
       "      <td>99.43</td>\n",
       "      <td>32702028</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-20</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.46</td>\n",
       "      <td>99.74</td>\n",
       "      <td>99.96</td>\n",
       "      <td>26275968</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-19</th>\n",
       "      <td>99.56</td>\n",
       "      <td>100.00</td>\n",
       "      <td>99.34</td>\n",
       "      <td>99.87</td>\n",
       "      <td>23779924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-18</th>\n",
       "      <td>98.70</td>\n",
       "      <td>100.13</td>\n",
       "      <td>98.60</td>\n",
       "      <td>99.83</td>\n",
       "      <td>36493867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-15</th>\n",
       "      <td>98.92</td>\n",
       "      <td>99.30</td>\n",
       "      <td>98.50</td>\n",
       "      <td>98.78</td>\n",
       "      <td>30136990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-14</th>\n",
       "      <td>97.39</td>\n",
       "      <td>98.99</td>\n",
       "      <td>97.32</td>\n",
       "      <td>98.79</td>\n",
       "      <td>38918997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-13</th>\n",
       "      <td>97.41</td>\n",
       "      <td>97.67</td>\n",
       "      <td>96.84</td>\n",
       "      <td>96.87</td>\n",
       "      <td>25892171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-12</th>\n",
       "      <td>97.17</td>\n",
       "      <td>97.70</td>\n",
       "      <td>97.12</td>\n",
       "      <td>97.42</td>\n",
       "      <td>24167463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-11</th>\n",
       "      <td>96.75</td>\n",
       "      <td>97.65</td>\n",
       "      <td>96.73</td>\n",
       "      <td>96.98</td>\n",
       "      <td>23794945</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2016-07-29  104.19  104.55  103.68  104.21  27733688\n",
       "2016-07-28  102.83  104.45  102.82  104.34  39869839\n",
       "2016-07-27  104.26  104.35  102.75  102.95  92344820\n",
       "2016-07-26   96.82   97.97   96.42   96.67  56239822\n",
       "2016-07-25   98.25   98.84   96.92   97.34  40382921\n",
       "2016-07-22   99.26   99.30   98.31   98.66  28313669\n",
       "2016-07-21   99.83  101.00   99.13   99.43  32702028\n",
       "2016-07-20  100.00  100.46   99.74   99.96  26275968\n",
       "2016-07-19   99.56  100.00   99.34   99.87  23779924\n",
       "2016-07-18   98.70  100.13   98.60   99.83  36493867\n",
       "2016-07-15   98.92   99.30   98.50   98.78  30136990\n",
       "2016-07-14   97.39   98.99   97.32   98.79  38918997\n",
       "2016-07-13   97.41   97.67   96.84   96.87  25892171\n",
       "2016-07-12   97.17   97.70   97.12   97.42  24167463\n",
       "2016-07-11   96.75   97.65   96.73   96.98  23794945"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2016-07']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x178c295bb70>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OaTPRG2OWi0hGC6v+CtwJLHJaNht42RhTA+wWkSxgEvBV14uqlPKERetyOFhuTRz2j8++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\n4IvbsukwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178c2c649b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x178c2964748>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x178c2abc6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].resample('M').mean().plot(kind='bar')"
   ]
  }
 ],
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